A. Field of Invention
This invention generally relates to tomographic imaging systems and methods for exact reconstructing three/four dimensional (volumetric/dynamic) images from the data obtained in a non-standard scan, such as a non-standard spiral or non-standard saddle or a general piecewise smooth scan.
B. Description of Prior Art
Cardiovascular diseases (CVDs) are pervasive (American Heart Association 2004). CVD is the number one killer in the western world. The cost of the health care for CVD is skyrocketing. In 2004, the estimated direct and indirect cost of CVD was $368.4 billion.
Coronary artery disease is a leading cause of death as a result of a myocardial infarct (heart attack) without any symptom. Because of its ultra high temporal resolution, electron-beam CT (EBCT) can quantify calcification in the arteries, which is an indication of atherosclerosis. EBCT scanners are now considered instrumental for detecting early heart diseases and are a centerpiece of preventive cardiology programs.
Just as tomographic equipment is needed in patient studies, micro-tomographic devices are needed in small animal studies. In order to understand etiology and pathogenesis of CVD, such as high blood pressure, coronary artery diseases, congestive heart failure, stroke and congenital cardiovascular defects, as well as to develop effective prevention and treatment strategies, small animals have become some of the most common models of human diseases.
Although there has been an explosive growth in the development of micro-CT scanners, there has been no development of a micro-CT scanner that allows ultra fast in vivo imaging to study dynamic processes in high spatial and contrast resolution. As a primary example, prior to the present invention, cardiac micro-CT of the mouse was simply impossible.
In order to use these animal models fully and explore their phenotypes at the whole organ and whole animal levels, the extension of cardiovascular imaging and physiological methodologies to small animals, such as mice and rats, is imperative.
Over the last thirty years, computer tomography (CT) has gone from image reconstruction based on scanning in a slice-by-slice process to spiral scanning. From the mid 1980s to present day, spiral type scanning has become the preferred process for data collection in CT. Under spiral scanning a table with the patient continuously moves through the gantry while the source in the gantry is continuously rotating about the table. At first, spiral scanning used a one-dimensional detector array, which received data in one dimension (a single row of detectors). Later, two-dimensional detectors, where multiple rows (two or more rows) of detectors sit next to one another, were introduced. In CT there have been significant problems for image reconstruction especially for two-dimensional detectors.
For three/four-dimensional (also known as volumetric/dynamic) image reconstruction from the data provided by a spiral scan with two-dimensional detectors, there are three known groups of algorithms: Exact algorithms, approximate algorithms, and iterative algorithms. While the best approximate algorithms are of Feldkamp-type, the state of the art of the exact algorithms is the recently developed Katsevich algorithm.
Under ideal circumstances, exact algorithms can provide a replication of a true object from data acquired from a spiral scan. However, exact algorithms can require a larger detector array, more memory, are more sensitive to noise, and run slower than approximate algorithms. Approximate algorithms can produce an image very efficiently using less computing power than exact algorithms. However, even under typical circumstances they produce an approximate image that may be similar to but still different from the exact image. In particular, approximate algorithms can create artifacts, which are false features, in an image. Under certain circumstances these artifacts can be quite severe.
To perform the long object reconstruction with longitudinally truncated data, the spiral cone-beam scanning mode and a generalized Feldkamp-type algorithm were proposed by Wang and others in 1991. However, the earlier image reconstruction algorithms for that purpose are either approximate or exact only using data from multiple spiral turns.
In 2002, an exact and efficient method was developed by Katsevich, which is a significant breakthrough in the area of spiral cone-beam CT. The Katsevich algorithm is in a filtered-backprojection (FBP) format using data from a PI-arc (scanning arc corresponding to the PI-line and less than one turn) based on the so-called PI-line and the Tam-Danielsson window. The principle is that any point inside the standard spiral or helical belongs to one and only one PI-line. Any point on the PI-line can be reconstructed from filtered data on the detector plane with the angular parameter from the PI-arc. In 2003, a backprojected-filtration algorithm (BPF) was developed for helical cone-beam CT based on the Katsevich algorithm by exchanging the order of integrals. For important biomedical applications including bolus-chasing CT angiography and electron-beam CT/micro-CT, generalization of the exact cone-beam reconstruction algorithms from the case of standard spirals to the case of nonstandard spirals and other scanning loci is desirable and useful.
Although the current Katsevich-type algorithms are known for a standard spiral scan, there are no known closed-form algorithms, systems, devices and methods that can reconstruct an image exactly or quasi-exactly from data acquired in a non-standard spiral scan or a general piecewise smooth scan.
Cone-beam CT along non-standard spirals is much more flexible than standard spiral CT in biomedical imaging applications. Algorithms, systems, devices and methods are needed to achieve FBP or BPF reconstructions in non-standard spiral cone-beam scanning cases. Particularly, non-standard spiral EBCT and micro-CT systems, devices and methods are needed to facilitate dynamic volumetric imaging. Such imaging systems, devices and methods can be used in small animal and patient imaging, including cardiac imaging, bolus-chasing CT angiography, and other applications.